The application of machine learning (ML) tech- niques to electronic health records (EHR) is gaining more and more attention as a method to extract valuable information that has the potential to enhance the decision-making process within the healthcare domain. A useful approach comes from the fed- erated learning (FL) scenario, which facilitates the decentralised training of machine learning models using datasets that are stored locally, hence eliminating the necessity of data aggregation on a central server. Federated learning also ensures data privacy because the federated devices do not share the actual data and store it locally. It becomes a useful tool when integrated with blockchain technology, which provides some properties such as immutability and traceability that are useful to enhance the security of such applications. With the growing use of IoT healthcare (IoHT) devices, it is becoming challenging to manage them centrally and ensuring the healthcare data privacy. In this work, we propose an architecture to address the scalability issue related to the healthcare data management for federated learning networks with a sharding-based blockchain technique. We discuss some basic properties and report some results also coming from the implementation in Hyperledger Fabric.

A Sharded Blockchain Architecture for Healthcare Data / J.Z. Shahid, S. Cimato, Z. Muhammad - In: COMPSAC / [a cura di] H. Shahriar, H. Ohsaki, M. Sharmin, D. Towey, AKM J. A. Majumder Yoshiaki Hori, J.-J. Yang, M. Takemoto, N. Sakib, R. Banno, S. Iqbal Ahamed. - [s.l] : Institute of Electrical and Electronics Engineers (IEEE), 2024. - ISBN 979-8-3503-7696-8. - pp. 1794-1799 (( Intervento presentato al 48. convegno Annual Computers, Software, and Applications Conference : 02-04 July tenutosi a Osaka (Japan) nel 2024 [10.1109/compsac61105.2024.00283].

A Sharded Blockchain Architecture for Healthcare Data

J.Z. Shahid
Primo
;
S. Cimato
Penultimo
;
2024

Abstract

The application of machine learning (ML) tech- niques to electronic health records (EHR) is gaining more and more attention as a method to extract valuable information that has the potential to enhance the decision-making process within the healthcare domain. A useful approach comes from the fed- erated learning (FL) scenario, which facilitates the decentralised training of machine learning models using datasets that are stored locally, hence eliminating the necessity of data aggregation on a central server. Federated learning also ensures data privacy because the federated devices do not share the actual data and store it locally. It becomes a useful tool when integrated with blockchain technology, which provides some properties such as immutability and traceability that are useful to enhance the security of such applications. With the growing use of IoT healthcare (IoHT) devices, it is becoming challenging to manage them centrally and ensuring the healthcare data privacy. In this work, we propose an architecture to address the scalability issue related to the healthcare data management for federated learning networks with a sharding-based blockchain technique. We discuss some basic properties and report some results also coming from the implementation in Hyperledger Fabric.
Blockchain; Data Security; Federated Learning; Healthcare; Scalability; Sharding;
Settore INFO-01/A - Informatica
Settore IINF-05/A - Sistemi di elaborazione delle informazioni
   SEcurity and RIghts in the CyberSpace (SERICS)
   SERICS
   MINISTERO DELL'UNIVERSITA' E DELLA RICERCA
   codice identificativo PE00000014

   SAFEST: Trust assurance of Digital Twins for medical cyber-physical systems
   SAFEST
   MINISTERO DELL'UNIVERSITA' E DELLA RICERCA
   20224AJBLJ_002
2024
Institute of Electrical and Electronics Engineers (IEEE)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1104748
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